Monthly Archives: April 2020

Week 9_2: GAN Case Study —— Yunhao Ye (Edmund)

I found two projects on the internet. The first one is an interesting project, it tries to teach the model to generate a random icon of Homer Simpson style. 

And here is a gif showing its result from epoch 0 to epoch 300.

Here is a gallery of the final images it gets.

Another project is a useful and practical one, its title is ‘Quick and Easy Time Series Generation with Established Image-based GANs’. Then briefly search on the internet what time series is. In Wikipedia, it is described as a series of data points indexed (or graphed) in time order.

Examples:

So the purpose of this project is to generate this kind of graphs, but it also means that these datas do not have a concrete source and they are forged by the model.

But why we need to do that? It says that many surveys in scientific or financial areas need lots of datas while those datas are protected by the privacy of people. So the researcher cannot get abundant datasets. And then they may need this technique to get more useful data for their survey.

Here is its basic structure

Here is its final result and comparison with real time series

I have also tried the Big GAN Colab codes on my own and have generated these videos.

 

Week 9_1: Neural Style Transfer Case Study —— Yunhao Ye (Edmund)

When I do my research on the internet, I found that most images created by Neural Style Transfer are generated from the content of a photo and the style of an artistic painting. So I wonder why there is not somebody generate images with reverse way, which means with the content of a painting and the style of a photo. And I began to search similar project.

Unfortunately, I cannot find a project try to generate this kind of images, but I do find a project generate images with the content of a photo and the style of another photo. The model is called Deep Photo Style Transfer. Basically, it is very just another model of Neural Style Transfer, but the designer make it fits with this special patter with the two inputs are both photo. Here are some images I get from its essay.

I think the effect is pretty good, the result is as satisfactory as the Fast Style Transfer in RunwayML. And I think this model is also something worth to try since we do not always fetch a style from a painting.

And I have found a website on which you can generate your own Neural Style Transfer images based on the input content image and style image. So I try to continue my idea. I first use the famous painting ‘The Starry Night’, which is the most popular style image used by people, as my content image. And I chose a photo of the starry sky as my style image.

(content)

(style)

(result)

I also tried this with another famous painting —— ‘The Great Wave off Kanagawa’ and a image of a huge wave.

(content)

(style)

(result)

When I try to generate my own images based on the Fast Style Transfer model on RunwayML, I try to provide content images which has special content. Since most of the content images people provide are typical photos composed with background and individual objects, I want to choose those images which do not make much sense to us. By doing this, I want to see how the model identify the content of those images while we cannot describe their content clearly.

1.

(content)

(result)

For this image, the model does not change the style of the line, instead, it add more details to the area surrounded by the lines. 

2.

(content)

(result)

For this image, the model kind of recognize each character as a block and add some features to it but it cannot get the clear structure of the character. And it also stylize the whole background.

3.

(content)

(result)

If we provide image with only texts with larger characters, the model then can recognize the structure of each character and create another style of fonts.

4.

(content)

(result)

For this image, the model does not seem pay much attention to the QR code. I feel like it just paint the original style image on or under this content image.

Week 8: Deep Dream Case Study —— Yunhao Ye (Edmund)

When discussing the Deep Dream during this week’ s lecture, it reminds me of the Uncanny Valley, since we can easily modify the human features and replace them with others’ using this technique.

Uncanny Valley is a hypothesized relationship between the degree of an object’s resemblance to a human being and the emotional response to such an object. The concept of the uncanny valley suggests that humanoid objects which imperfectly resemble actual human beings provoke uncanny or strangely familiar feelings of eeriness and revulsion in observers. Technically, it is not a strict scientific rule, but it is commonly used in our daily life. (below is a famous graph explaining it)

So I try to collect Deep Dream images which can cause horror or disgust to us with because of the Uncanny Valley. I categorize them into three types.

The first type is the most kwon form of both the Uncanny Valley and Deep Dream, it is the human with animals’ features.

Personally, I do not feel uncomfortable when I look at these images, I think it may be because we can only use dog faces and they are so familiar for us so we do not feel strange to see them.

The second type is the animal with human’s features. Actually, I cannot find Deep Dream images belonging to this type, probably because there is no human layer in the technique. But I suggest if we can produce those images, it will be much more scary then the first type. Here is an example (not a Deep Dream image), I think the puppy with human’s face gives me more shock then the man with dog’s face.

The third type are the animals with irregular body structure. This is not the area of Uncanny Valley since it is not related to human. But in my opinion, it is the animal’s extension of Uncanny Valley which can cause a similar effect.

It can be seen that the animal’s natural physical structure has been changed greatly, which provides a feeling of sutured monsters.

Additionally, I have found a website which produces Deep Dream images based on the food images. It is very creative and it really impress me a lot.

These kind of images can make people very disgusting, I think it is because we have high demand and expectation for food since it will go deep into our body. So if our food looks strange, we will not feel physically comfortable.

And for my own exploration, I tried to make different font styles with the help of Deep Dream. But the result is not very satisfactory, the font is too thin so it is hard to add much details on the the texts. Also, it will cause an error when using png file, so I cannot discard the white background.

Here is the original image I used

Here are the Deep Dream images I got